A reinforcement-learning approach to reactive control policy design for autonomous robots

Within the field of robotics, much recent attention has been given to control techniques that have been termed reactive or behavior-based. The design of such control systems for even a remotely interesting task is typically a laborious effort, requiring many hours of experimental “tweaking” as the actual behavior of the system is observed by the system designer. In this paper, the authors present a neural-based reinforcement learning approach to the design of reactive control policies in which the designer specifies the the desired behavior of the system, rather than the control program that produces the desired behavior